2091results about How to "Easy to capture" patented technology

The invention provides a multi-target collaborative optimization control method and system for an igniter robot production line, and relates to the technical field of automatic control, and the method comprises the steps: collecting real-time operation parameters of the production line, carrying out the wavelet denoising, feature extraction and fusion, inputting a hierarchical asynchronous dominant actor commentator model, and in a strategy network layer, carrying out the wavelet denoising, feature extraction and fusion; a robot trajectory planning scheme is optimized and calculated through an improved near-end strategy, a production line speed control scheme is obtained through a layered dual-state action value function in combination with an adaptive Coubeck-Leibler divergence constraint, an energy consumption distribution scheme is obtained based on pseudo-parallel Monte Carlo tree search and an improved soft actor commentator algorithm, and an energy consumption control scheme is obtained. The value network layer evaluates an energy consumption distribution scheme and generates a combined control strategy, after the strategy is executed, a cycle converter model is improved to evaluate production indexes, model parameters are dynamically optimized, and multi-target collaborative optimization is achieved.

The invention discloses a full-period traffic flow prediction method based on spatial-temporal feature deep fusion, and aims to deeply integrate complex spatial-temporal dependence of traffic flow, form composite traffic situation perception and realize traffic multi-period comprehensive flow prediction. The method comprises the following steps: firstly, through spatio-temporal information comprehensive representation, extracting time sequence characteristics of multiple time modals and constructing a concrete real spatial relationship; in order to further construct a digital urban traffic flow prediction method fusing multi-scale and multi-granularity comprehensive elements, a comprehensive traffic flow prediction model deeply fusing short-term space-time dependence is provided for short-term traffic flow prediction, so that the sensitivity to instantaneous flow change is improved; the short-term dynamic change of the traffic flow in the multifunctional airspace node is fully modeled; in addition, a deep airspace deconstruction and time sequence feature fusion model is designed for medium and long term traffic prediction and deep extraction of complex traffic modes. Simulation results show that the method provided by the invention is superior to the existing traffic flow prediction technology.

The invention relates to the field of source network load storage, in particular to a real-time coordination control method and system suitable for regional-level source network load storage. The technical problems that in the prior art, a regional-level source network load storage real-time coordination control method may cause missing and repetition of historical data and generation of abnormal values, resulting in influence on the accuracy of a prediction model, and may cause weak generalization ability and influence the prediction precision due to improper feature selection or unreasonable model selection are solved; according to the technical scheme, the real-time load-storage coordination control method and system suitable for the regional-level source network comprises the steps that predicted output power of new energy such as a photovoltaic power station and a wind power plant and predicted data of a power grid load are obtained in real time, and short-term and medium-term predicted output of the new energy is calculated through a prediction algorithm according to historical data and real-time data; according to the method, the data quality is improved through data arrangement, and the prediction precision and efficiency are improved by enhancing the generalization ability of the model and combining short-term and medium-term prediction.

The invention provides a riverbed evolution prediction method based on multi-source data fusion, and belongs to the technical field of hydrological resource monitoring. The method comprises the following steps: acquiring and fusing multi-source data by using different devices, performing data preprocessing to form unified three-dimensional grid data, and then generating a multi-scale feature map based on a designed feature extraction network; based on the designed multi-channel convolutional neural network, learning and extracting features on multiple channels at the same time, and performing hierarchical expression and fusion on different information sources; and finally, based on the designed time sequence regression network, extracting a complex dependency relationship in the time sequence through a double-layer LSTM structure, learning residual error information through an adaptive residual error correction module to improve prediction precision, and obtaining a final prediction result. The method shows relatively high applicability and robustness in a complex river channel environment, and can cope with various river bed form changes caused by natural factors and human activities through multi-source data fusion and an innovative feature extraction and regression method.

The invention discloses a human body behavior recognition method and system based on a space-time fusion network, and relates to the technical field of behavior recognition, and the method comprises the steps: obtaining a human body skeleton sequence, carrying out the joint position coding of the sequence, taking the coded sequence as an input feature, inputting the input feature into the space-time fusion network based on graph convolution and Transform, and outputting a human body behavior recognition result; wherein the space-time fusion network comprises a plurality of groups of graph convolution Transform space fusion modules and multi-scale time fusion modules which are connected in sequence, after input features are input, local structure features and global space features are extracted through the parallel adaptive graph convolution module and Transform module respectively, feature fusion is carried out through the correlation fusion module, and the local structure features and the global space features are fused through the correlation fusion module. And the fused features are input into a multi-scale time fusion module, and are subjected to feature extraction through a multi-scale convolution branch and a Transform branch and then spliced, so that a more accurate human behavior recognition result is output.

The invention relates to the technical field of machine learning, in particular to a supervised time sequence water level data generation method and system and a storage medium. The method comprises the following steps: acquiring multi-source hydrological monitoring data; performing abnormal value identification and data preprocessing according to the hydrological monitoring data to generate standardized hydrological basic data; constructing a data generation model of the conditional generative adversarial network based on the standardized hydrological basic data; hydrological variable driven condition feature regulation and control are carried out on the data generation model, parameter optimization of the generation network is carried out, and hydrological adaptability enhanced condition input feature data are generated; and injecting the condition input characteristic data into the data generation model to generate initial water level time sequence data. According to the method, the rare extreme hydrological events are oversampled by using a hierarchical importance sampling and self-service method (Bootstrap), so that the performance of the generative model in rare event prediction is remarkably enhanced.

The invention discloses a brain tumor MRI image segmentation model and method based on an improved Swin UNETR network, and belongs to the technical field of image segmentation. The brain tumor MRI image segmentation model is obtained by improving a Swin UNETR as a baseline model and comprises an encoder, a jump connection and a decoder, the decoder comprises a plurality of convolution fusion modules and an attention mechanism module, feature information of different levels is fused in the decoder by using the plurality of convolution fusion modules, and the attention mechanism module is used for identifying the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the feature information of the different levels of the convolution fusion modules. And the attention mechanism module is used to focus the feature information obtained by the plurality of convolutions. By adopting the scheme of the invention, the problem that the existing medical image segmentation technology based on deep learning is insufficient in brain tumor MRI image segmentation precision and is difficult to realize accurate segmentation especially in the face of different types of tumors and variability can be effectively solved, the calculation amount is small, and the image segmentation efficiency can be effectively improved.

The invention belongs to the field of artificial intelligence and finance, and relates to a text classification method, which comprises the following steps: preprocessing an original text data set, labeling classification labels to obtain a labeled text data set, inputting a training set into a large language model for keyword extraction to obtain a candidate keyword set, cleaning the candidate keyword set to obtain a classification keyword set, constructing an instruction template based on the classification keyword set, and inputting the training set and the instruction template into a large language model to obtain a prediction classification result; and based on a prediction classification result and a loss function between the classification labels, performing fine tuning on the large language model to obtain a fine tuning model, evaluating that the fine tuning model is qualified to obtain a text classification model, and inputting the to-be-classified text into the text classification model for classification. The invention further provides a text classification device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the tagged text data set can be stored in a block chain. According to the method, the text classification accuracy and stability can be improved.

The invention discloses an optical cable routing anti-intrusion method and system, and the method comprises the following steps: laying a sensor network at key nodes along an optical cable, monitoring the data of a surrounding environment in real time, constructing an environment reference model, and integrating the model to a GIS platform, thereby achieving the risk assessment of different regions. Different monitoring frequencies and sensitivities are set for a high-risk area and a low-risk area according to an evaluation result, an anomaly detection module compares real-time signal data with an environment reference model, high-credibility abnormal signals are screened out through self-adaptive threshold adjustment, and when a physical barrier module detects abnormal external force, the physical barrier module sends out the abnormal external force. Triggering the area and the adjacent area to enter a high-frequency monitoring mode to form a dense monitoring area, positioning an intrusion position through a sensor network and a GIS platform, generating a path prediction model, and dynamically adjusting the frequency, sensitivity and alarm threshold value of the monitoring area based on data analysis and GIS geographic information. The adaptability and the resource configuration efficiency of the system in different environments are improved.

The invention relates to the technical field of ozone concentration prediction, in particular to an ozone concentration prediction method, device, equipment and medium based on a space-time diagram convolutional network model.The method comprises the steps that historical ozone concentration and meteorological data of a target area are obtained, and training and prediction samples are constructed after standardization; a space diagram structure is established, nodes represent monitoring points, and edges represent the monitoring points with correlation exceeding a threshold value; extracting a spatial dependency relationship by adopting GCN and GAT, and generating time sequence data; tCN or RNN is adopted to extract time-dependent features, and spatial-temporal feature comprehensive representation is obtained; training a model by utilizing the training sample and the spatial-temporal characteristics, adopting MSE (mean square error) as a loss function, adjusting parameters by an Adam optimizer, and optimizing hyper-parameters to minimize errors; newest meteorological and historical data are input, future ozone concentration is predicted, GNN extracts spatial features, RNN processes a time sequence, time sequence data can be processed in parallel through convolution operation, and high calculation efficiency is achieved.

The invention discloses a multi-scale feature fusion triple branch network method for multi-organ segmentation, and the method comprises the following steps: extracting local features through a CNN, and extracting global features through Transform; the features extracted by the CNN and the Transfomer are processed by using space attention and channel attention respectively, the processed features are fused, and output features are obtained through a feature processing module; the method comprises the following steps: setting a mask mechanism Smart Mask, improving a fixed mask mode in a Swin Transform, and capturing a long-distance dependency relationship; according to the method, global features in a medical image are learned through parallel Vision Mama, features obtained after CNN and Transform fusion are guided, and a segmented image is obtained. The CNN, Transform and Mamba encoders are combined, so that efficient medical image segmentation is realized. And the introduced parallel Vision Mama layer increases a small number of parameters and enhances the global context capture capability at the same time, so that the performance and efficiency of the model are remarkably improved.

The invention provides an intelligent evaluation method for cognitive function detection, and relates to the technical field of cognitive function detection. Comprising the following steps: constructing a virtual reality situation with a plurality of virtual reality tasks, carrying out customized setting on the virtual reality situation, collecting physiological data and behavior data of a testee in real time, recording performance indexes of the testee, collecting subjective feedback of the testee in the virtual reality tasks, constructing an evaluation model based on a support vector machine, and evaluating the virtual reality tasks according to the evaluation model. And dynamically adjusting the task difficulty according to an evaluation result, constructing a multi-dimensional cognitive file of the testee, and constructing a cognitive function evaluation model based on a long short-term memory network to evaluate the cognitive function of the testee. According to the invention, by setting different types of cognitive tasks, the cognitive function condition of the testee can be evaluated more comprehensively; by performing fusion analysis on the physiological signals and the behavior data, multiple dimensions of the cognitive function can be better captured, and comprehensiveness and accuracy of cognitive function detection are achieved.

The invention discloses a flight control method and system of an unmanned aerial vehicle tracking technology, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the following steps: before an unmanned aerial vehicle takes off, starting an airborne sensor in advance, executing parameter initialization and calibration operation, and installing a magnetic marker on a tracking target; acquiring the body position of the unmanned aerial vehicle in the flight process, and sensing the target position of the tracking target in real time by adopting a multi-sensor data fusion algorithm; obstacles existing in the flight path are detected in real time, a three-dimensional environment model is integrated, and the motion state of a tracking target is captured in combination with a follow-up visual tracking algorithm; target tracking data are stored and fed back in real time, and the battery state and the energy consumption speed of the unmanned aerial vehicle are monitored. By combining the magnetic guidance technology, multi-sensor data fusion and a follow-up visual tracking algorithm, high-precision target tracking can be realized in a dynamic complex environment, meanwhile, obstacles are effectively avoided, and the flight stability and tracking precision of the unmanned aerial vehicle are ensured.

The invention discloses a photovoltaic power ultra-short-term prediction method and system based on itransfomer, and relates to the technical field of photovoltaic power generation. A traditional prediction method is relatively limited in precision under the conditions of rapid cloud layer change and abnormal local weather. The method comprises the steps of weather data collection and processing, feature construction based on weather data, iTransform model construction and training, and photovoltaic power ultra-short-term prediction. According to the technical scheme, the multi-dimensional features are constructed by processing the weather data, and efficient feature extraction and photovoltaic power prediction are realized in combination with the multi-head attention mechanism and nonlinear mapping of the iTransform model. Model training is combined with actual measurement data and weather forecast correction, accuracy and reliability of input data are ensured, prediction precision is improved, high instantaneity and adaptability are achieved, high accuracy, instantaneity and reliability are achieved under the complex weather processing condition, powerful support is provided for power grid dispatching, and especially in the aspect of ultra-short-term photovoltaic power prediction, the method has a good application prospect. And the safety and reliability of a power system can be effectively improved.

The invention relates to the field of geological disaster prediction, and discloses a geological disaster real-time monitoring and early warning system based on Internet of Things and artificial intelligence, and the system comprises a sensing module which is used for collecting multi-source data of geological disasters in real time through all Internet of Things sensors, and visually displaying potential geological disaster hidden danger signs on the ground surface; the transmission module is used for transmitting various data and guaranteeing the data transmission security by adopting an encryption technology; the processing module is used for sorting and processing the multi-source data and converting the multi-source data into processing data in a standardized data format; and the model module is used for processing data and applying a model fusing a physical principle and a machine learning algorithm. An advanced early warning model is constructed by applying an artificial intelligence technology and fusing a physical principle and a machine learning algorithm, a deep learning optimization unit customizes a strategy for geological disaster images and time sequence data characteristics, and a knowledge graph unit introduces geological domain knowledge to assist modeling.

The invention provides a forged voice detection method and system based on multi-task learning, and the method comprises the steps: carrying out the preprocessing of original audio data, and extracting a primary feature through a pre-training model; then, inputting the primary features into a forged voice detection model, and carrying out multi-task learning; thirdly, jointly optimizing classification loss and reconstruction loss, and training the forged voice detection model until convergence; and finally, performing authenticity detection on to-be-detected audio data by using the trained forged voice detection model, and distinguishing whether the to-be-detected audio data is forged voice or not. The method is suitable for acoustic scenes in which the number of real voices and the number of counterfeit voices are unbalanced, is also suitable for detecting counterfeit voices of unknown counterfeit types, and has good universality and generalization.

The invention discloses a multi-class defect detection method and device based on a variational auto-encoder and a denoising network, and the method comprises the steps: obtaining an industrial product surface defect image, carrying out the preprocessing, and constructing a training set and a verification set; the training process of the method is divided into two stages: in the first stage, a preprocessed training image is input into a variational auto-encoder for training, feature distribution of the image is learned, and features of a normal image are captured by reconstructing the image; in the second stage, a denoising network and a feature guiding module are used for conducting secondary reconstruction on image features in the feature space of the variational auto-encoder, and therefore the reconstruction capacity and the multi-class reconstruction capacity of the model are enhanced; in the reasoning stage, multi-scale features of an original image and a reconstructed image are extracted through a feature extractor, feature comparison is carried out, and an abnormal scoring graph is generated. According to the method, normal information is reserved while the reconstruction capability of the variational auto-encoder is enhanced, and defect missing detection caused by reconstruction failure is effectively reduced.

The invention relates to the technical field of data processing, and discloses a wind wave flow prediction method and system based on meteorological simulation. The method comprises the following steps: performing atmospheric mode simulation on initial condition data and boundary condition data to obtain sea surface wind field data and atmospheric parameter data; performing post-processing and interpolation on the sea surface wind field data and the atmospheric parameter data to obtain wind wave model driving force field data; carrying out wind wave model coupling and simulation on the wind wave model driving force field data to obtain wind wave prediction result data; performing ocean current model coupling and simulation on the wind wave prediction result data and the wind wave model driving force field data to obtain ocean environment prediction result data; and performing data fusion and data assimilation processing on the marine environment prediction result data, the storm prediction result data and the storm model driving force field data to obtain storm flow prediction data, thereby improving timeliness and accuracy of storm flow prediction based on meteorological simulation.

The invention discloses a knowledge graph-driven sales data multi-dimensional analysis and visualization method and system. The method comprises the following steps: constructing a sales domain knowledge graph containing products, customers and transaction entities; on the basis of the knowledge graph, preprocessing the original sales data to obtain preprocessed sales data; according to a knowledge graph and the preprocessed sales data, realizing time sequence adaptive multi-view collaborative graph embedding, and obtaining graph embedding features; fusing the graph embedding feature with the traditional sales data feature to obtain a fused feature; performing multi-dimensional analysis based on the fusion features to obtain an analysis result; performing visual presentation on the analysis result to generate visual data; on the basis of visual data, an interactive exploration interface is provided, deep analysis and insight of data are achieved, by introducing the knowledge graph technology, the complex relation between entities can be effectively captured and utilized, the association rule mining effect is remarkably improved, and the association rule mining efficiency is improved by 77.8% compared with a traditional method.

The invention provides a drug-disease association prediction method and system based on heterogeneous node sequence representation, and belongs to the technical field of drug discovery in bioinformatics. Constructing a drug similarity network and a disease similarity network; constructing a heterogeneous biological interaction network by using the drug similarity network and the disease similarity network, and obtaining a first-order neighbor subgraph of the target node; splitting the first-order neighbor subgraph of the target node into a plurality of meta-relation bigraphs according to the meta-relation type, performing intra-domain message propagation and aggregation in each meta-relation bigraph, and updating the semantic features of the target node; fusing the semantic features of the target nodes in a cross-domain manner, updating the sequence features of the target nodes, and aggregating the features; obtaining vector representation of a target node through a multi-head self-attention mechanism by using the updated sequence features; and inputting the vector representations of the drug nodes and the disease nodes into a multi-layer perceptron to predict the drug-disease association probability. Semantic information confusion is effectively avoided, and then the accuracy of model prediction is improved.

The invention discloses an image denoising method based on double-branch feature fusion, and the method specifically comprises the steps: 1, carrying out the collection of an image, and carrying out the preprocessing of the collected image; 2, a denoising network with double-branch feature fusion is built, the network comprises a generation network and a discrimination network, and authenticity discrimination is carried out on a denoised image generated by the generation network and a real image through the discrimination network; step 3, adversarial training is carried out on the generative network and the discriminant network to obtain a trained dual-branch feature fusion denoising network; and step 4, performing denoising processing on the image by using the trained generative network. The method is remarkably superior to a traditional denoising method in denoising quality, model generalization ability and operation time, different types of noise can be effectively removed, and more image details and structure information are reserved.

The invention discloses an air quality prediction method for mining a space-time attention mechanism based on multiple relationships. The method comprises the following steps: constructing a pollutant concentration change matrix and a meteorological change matrix; acquiring geographical location information data, site feature data and time sequence data of the monitoring sites; determining edges of a space-time diagram according to the pollutant concentration data set and the historical meteorological data set, determining nodes of the space-time diagram according to the geographic position information data, and constructing a space-time diagram structure; determining an adjacent matrix according to the space-time diagram structure, and normalizing the adjacent matrix according to a second normalization formula; training the neural network model to obtain an air quality prediction model; and inputting to-be-detected target data into the air quality prediction model, and outputting target predicted air quality data through the air quality prediction model. According to the invention, the accuracy and timeliness of predicting pollutants in the air can be improved.

The invention discloses a semantic understanding-based technical literature multi-dimensional analysis method, which comprises the following steps of: performing text vectorization processing on text data by utilizing a Sension-BERT model to generate dense vector representation of a text; vector dimensionality reduction is carried out by adopting UMAP, and redundant features are removed; performing unsupervised clustering analysis by using HDBSCAN to generate a clustering result; extracting a subject term from a clustering result by adopting a TF-ICF method; and carrying out multi-dimensional analysis on the clustering result. Compared with the prior art, a comprehensive and efficient patent analysis tool is provided through an advanced semantic understanding patent clustering method, so that deeper interpretation and analysis of technical literatures are realized while the cost benefit is ensured.

The invention discloses a surface defect detection method and system based on feature focusing refinement, and relates to the technical field of defect detection. The method comprises the steps that a target feature focusing refining defect detection model comprises a target edge focusing feature extraction network, a target multiplexing fusion semantic network, a target semantic-channel-space weighted refining network and a target detection layer; outputting a plurality of defect edge multi-scale focusing feature vectors based on the to-be-detected image through a target edge focusing feature extraction network; inputting each defect edge multi-scale focusing feature vector into a target multiplexing fusion semantic network to correspondingly generate a plurality of multiplexing fusion semantic features; correspondingly determining a plurality of semantic-channel-space weighted refinement features based on each multiplexing fusion semantic feature by adopting a target semantic-channel-space weighted refinement network; and outputting a defect detection result of the to-be-detected image based on each semantic-channel-space weighted refinement feature through a target detection layer. And the small target defect detection precision is improved.

The invention discloses a log anomaly detection method, system and device and a medium, and relates to the field of security technology early warning, and the method comprises the steps: obtaining to-be-detected log sequence data of an electric power mobile micro-service system, inputting the to-be-detected log data into a trained anomaly detection model, and obtaining an anomaly detection result; the anomaly detection model is constructed based on a log data set and comprises a data processing module and a local attention processing module; the data processing module is used for converting input log sequence data into semantic feature vectors; and the local attention processing module is used for performing semantic feature representation according to the semantic feature vector, performing anomaly detection on each semantic feature to obtain an anomaly detection result, and the method can accurately detect the log anomaly condition of the micro-service system.

The invention discloses a traditional Chinese medicine recommendation method based on knowledge driving and a residual attention network, and the method comprises the following steps: S1, a data collection and cleaning module: obtaining prescription text data, executing a standardized process for symptoms and traditional Chinese medicine terms in the prescription text data, and dividing a data set after the standardized process is executed into a training set, a verification set and a test set; s2, an entity pre-training module: extracting symptom semantic features by using a Word2vec training model to extract the features of symptom entities in the prescription and obtain overall context semantic representation, constructing a traditional Chinese medicine attribute knowledge graph, and fusing the natural attribute features of the traditional Chinese medicine as external knowledge into a recommendation model; s3, a multi-graph construction and entity feature learning module: according to a co-occurrence relationship between the symptoms and the traditional Chinese medicines, constructing a symptom-traditional Chinese medicine isomerism graph (SHHG), a symptom-symptom isomerism graph (SSIG) and a traditional Chinese medicine-traditional Chinese medicine isomerism graph (HHIG); respectively extracting symptom features, traditional Chinese medicine features and interaction features from a symptom-traditional Chinese medicine isomeric graph (SHHG), a symptom-symptom isocomposition (SSIG) and a traditional Chinese medicine-traditional Chinese medicine isocomposition (HHIG) by using a graph attention neural network, and enhancing entity features by using a residual structure; the feature fusion and traditional Chinese medicine recommendation module is used for fusing symptom features and traditional Chinese medicine features extracted from a symptom-traditional Chinese medicine isomerism graph (SHHG), a symptom-symptom isomerism graph (SSIG) and a traditional Chinese medicine-traditional Chinese medicine isomerism graph (HHIG) to obtain final symptom features and traditional Chinese medicine features, and expressing the symptom features by using MLP to obtain a traditional Chinese medicine recommendation result. And finally recommending a proper traditional Chinese medicine set according to the given symptom set.

The invention relates to the technical field of wireless communication testing, and particularly discloses a Bluetooth headset anti-interference performance testing system which comprises an interference signal generation module, an interference signal dynamic simulation module, a signal stability monitoring and evaluation module and an anti-interference capability identification module. An interference signal comprising a modulation signal, a cross-band noise signal and a dynamic radio frequency signal is generated and injected into a test environment, and a real interference scene is simulated by using dynamic conversion of signal strength and frequency; the monitoring module obtains the intensity and frequency characteristics of the interference signal in real time, and comprehensively evaluates the signal stability based on an intensity abnormal coefficient and a frequency abnormal coefficient; and the anti-interference capability identification module calculates an anti-interference capability coefficient by using a support vector machine in combination with performance data of the Bluetooth earphone under different interference levels, and realizes graded evaluation of earphone performance.

The invention discloses a power grid communication equipment fault identification method and system based on multi-source heterogeneous data characterization learning. The method comprises the following steps: firstly, collecting various complex multi-source heterogeneous data containing fault power grid communication equipment and preprocessing the complex multi-source heterogeneous data; extracting deep semantic features by using a pre-trained feature extraction network and obtaining a feature vector sequence; capturing time features of the feature vector sequence through a bidirectional long-short-term memory network, and obtaining complete sequence feature representation through a full connection layer; performing low-rank multi-modal fusion on the complete sequence feature representation in combination with cross-modal Transform to obtain three types of output splicing construction joint representation, and obtaining a fusion feature by using a self-attention mechanism; and finally, constructing a fault identification model by using the fusion features. Through innovative data preprocessing and data fusion technologies and in combination with an advanced deep learning model, fault-related characteristics can be automatically learned from multi-source heterogeneous data, and intelligent identification of the fault state of the power grid communication equipment is realized.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

The invention discloses an optimized dispatching control method and system for transformer area energy storage, and relates to the technical field of energy storage dispatching, and the method comprises the following steps: integrating an intelligent load management system with an energy storage system, thereby monitoring the electrical load of a transformer area in real time, and feeding back the real-time data to the energy storage system; historical load data and electricity price data are collected, a prediction model is trained, future load and electricity price are predicted in real time, and prediction data are generated. And according to a prediction result, a preliminary energy storage system scheduling strategy is formulated, and a target function R is constructed, so that the target function R can dynamically adapt to changing load and electricity price conditions. According to the invention, through tight integration of the intelligent load management system and the energy storage system, real-time monitoring of the power grid load and efficient scheduling of the energy storage system are realized; in other words, the energy storage system can charge or discharge at the best time in combination with accurate load and electricity price prediction and scheduling strategy rules, power grid resources are utilized to the maximum extent, and unnecessary resource waste is avoided.